---name: finding-youtube-sponsorship-candidatesdescription: >Triggers when the user asks to: find YouTube channels to sponsor, discover YouTubers who acceptlicense: Apache-2.0metadata: author: apidojoversion: 1.0apify-actor: apidojo/youtube-scraper---

Finding YouTube Sponsorship Candidates

Discovers YouTube channels that are good fits for brand integrations. Channels with existing sponsor history are the most efficient outreach targets — they've already proven willingness to accept sponsorships.

Prerequisites

• APIFY_TOKEN environment variable set
• Optional: Apify MCP server installed

Inputs

Parameter	Type	Required	Default	Notes
startUrls	array	Optional	[]	YouTube URLs — channels, playlists, Shorts, search results
youtubeHandles	array	Optional	[]	YouTube channel handles (e.g. @kurzgesagt)
getTrending	boolean	Optional	false	Retrieve trending videos
keywords	array	Optional	[]	Search keywords
gl	string	Optional	us	Country code for results (e.g. US, GB)
hl	string	Optional	en	Language code (e.g. en, de)
uploadDate	string	Optional	all	Upload date filter: any, hour, today, week, month, year
duration	string	Optional	all	Duration filter: any, short, long
features	string	Optional	all	Feature filter: 4k, hd, live, cc, 3d, hdr, etc.
sort	string	Optional	r	Sort order for search results
maxItems	number	Optional	Unlimited	Maximum videos to return
customMapFunction	string	Optional	—	JavaScript function to transform each output object

Workflow

Progress:
- [ ] Step 1: Search YouTube for niche channel content
- [ ] Step 2: Collect channel handles from results
- [ ] Step 3: Enrich channel data
- [ ] Step 4: Score sponsorship fit
- [ ] Step 5: Deliver ranked outreach list

Step 1: Search for Niche Content

Recommended — run_actor.js (handles waiting, output, and file saving automatically):

# Quick answer (prints table to chat)
node scripts/run_actor.js \
  --actor "apidojo~youtube-scraper" \
  --input '{"param": "value"}'

# Save as CSV
node scripts/run_actor.js \
  --actor "apidojo~youtube-scraper" \
  --input '{"param": "value"}' \
  --output YYYY-MM-DD_results.csv --format csv

# Save as JSON
node scripts/run_actor.js \
  --actor "apidojo~youtube-scraper" \
  --input '{"param": "value"}' \
  --output YYYY-MM-DD_results.json --format json

APIFY_TOKEN must be set in environment or .env file.

If Apify MCP is available:

Tool: apify:run-actor
Actor: "apidojo~youtube-scraper"
Input:
{
  "searchKeywords": ["best [NICHE] tools", "[NICHE] review", "[NICHE] for beginners", "top [NICHE]"],
  "maxResults": 50,
  "type": "video"
}

REST API fallback:

curl -X POST   "https://api.apify.com/v2/acts/apidojo~youtube-scraper/runs?token=$APIFY_TOKEN"   -H "Content-Type: application/json"   -d '{
    "searchKeywords": ["best personal finance tools", "personal finance review"],
    "maxResults": 50,
    "type": "video"
  }'

Collect unique channelId and channelName values.

Step 2: Enrich Channel Data

If Apify MCP is available:

Tool: apify:run-actor
Actor: "apidojo~youtube-scraper"
Input:
{
  "startUrls": [{"url": "https://www.youtube.com/channel/[CHANNEL_ID]"}],
  "maxResults": 10,
  "type": "video"
}

Step 3: Score Sponsorship Fit

view_ratio = avg_views / subscriber_count

sponsorship_score = (view_ratio > 0.1 ? 1 : view_ratio / 0.1) * 0.30
                  + (subscriber_count in 10000..200000 ? 1 : 0.6) * 0.20
                  + (avg_comments / avg_views > 0.005 ? 1 : (avg_comments/avg_views)/0.005) * 0.20
                  + (has_sponsor_history ? 1 : 0) * 0.30

Sponsorship signal detection (in last 10 video titles/descriptions):

sponsor_count = count(videos where description contains ["sponsored by", "use code", "thanks to", "partner"])
has_sponsor_history = sponsor_count >= 1
repeat_sponsor = sponsor_count >= 3

Tier: TIER A ≥ 0.70 | TIER B 0.45–0.69 | TIER C < 0.45

Step 4: Edge Cases

• View count spike from one viral video: Use median views from last 10 videos, not mean; flag channels where max_views > 10× median
• Channel in adjacent but not target niche: Score niche alignment — percentage of last 20 videos in target niche
• Subscriber count stale: YouTube counts lag; use avg_views as the true reach proxy
• No description available: Skip sponsorship history check; score at 0.5 for that component

Output Format

# YouTube Sponsorship Candidates: [NICHE]
Channels evaluated: [N] | TIER A: [N] | TIER B: [N] | Date: [DATE]

## TIER A — Strong Sponsorship Candidates
| Channel | Subscribers | Avg Views | View Ratio | Sponsor History | Niche Fit | Score |
|---------|------------|-----------|------------|-----------------|-----------|-------|
| [name] | [N] | [N] | [X.XX] | [Yes/No/Repeat] | [%] | [0.XX] |

## TIER B — Secondary Candidates
| Channel | Subscribers | Avg Views | View Ratio | Last Sponsor |
|---------|------------|-----------|------------|-------------|

## Sponsorship Landscape in [NICHE]
- Channels already running sponsors: [N]/[N] evaluated ([X%])
- Most common sponsor in category: [brand name] (seen on [N] channels)
- Typical viewer demographic signal (from video titles): [description]

Troubleshooting

Results are all mega-channels (> 1M subs): Narrow the search query with "beginner" or "indie" qualifiers; or filter post-scrape by subscriber count. Niche too broad: Narrow to a sub-niche (e.g. "personal finance" → "fire movement", "crypto" → "Bitcoin long-term investing"). Can't detect sponsor history from descriptions: Sponsor language is sometimes hidden in video captions (not descriptions). This is a known limitation — supplement with manual check of top 5 candidates.